Patients with Non-Small-Cell Lung Cancer Harbouring a BRAF Mutation: A Multicentre Study Exploring Clinical Characteristics, Management, and Outcomes in a Real-Life Setting: EXPLORE GFPC 02-14
Bibliographic record
Abstract
Background: Mutations in BRAF are rare oncogene mutations, found in 2% of non-small-cell lung cancers (NSCLCS). Little information is available about the management of patients with BRAF-mutated nsclc, except for those included in clinical trials. We undertook the present study to assess the clinical characteristics, management, and outcomes of those patients in a real-life setting. Methods: This retrospective multicentre observational study included all patients with BRAF-mutated nsclc diagnosed between January 2012 and December 2014. Results: Patients (n = 59) from 24 centres were included: 57.6% men; mean age: 64.5 ± 14.5 years; 82% with a performance status of 0–1 at diagnosis; smoking status: 40.3% current, 32.6% former; 93% with adenocarcinoma histology; 75% stage iv; 78% with V600E mutations; 2 with EGFR and 2 with ALK co-mutations. Of the stage iv patients, 79% received first-line therapy (14.2% anti-BRAF), and 48% received second-line treatment (23.8% anti-BRAF). Response rate and progression-free survival were, respectively, 51.7% and 8.7 months [95% confidence interval (CI): 6.4 months to 15.2 months] for first-line therapy and 35.3% and 4.1 months (95% CI: 2 months to 10.9 months) for second-line treatments. The 2-year overall survival was 58.5% (95% CI: 45.8% to 74.8%). Outcomes in patients with stage iv nsclc harbouring BRAF V600E mutations (n = 32) did not differ significantly from those of patients with other BRAF mutations. Conclusions: In this real-world analysis, most nsclc patients with a BRAF mutation were men and current or former smokers. Survival appears to be better in these BRAF-mutated patients than in nsclc patients without an oncogenic driver.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".